Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

TL;DR

This paper introduces an interpretable deep learning model using spatiotemporal causal attention for water level forecasting, improving reliability and robustness over existing methods.

Contribution

It presents a novel transformer-based model that quantifies interpretability in water level prediction, addressing the lack of transparency in prior machine learning approaches.

Findings

Outperforms competing methods in interpretability and accuracy

Enhances robustness against distribution shifts

Aligns interpretability with common knowledge

Abstract

Accurate forecasting of river water levels is vital for effectively managing traffic flow and mitigating the risks associated with natural disasters. This task presents challenges due to the intricate factors influencing the flow of a river. Recent advances in machine learning have introduced numerous effective forecasting methods. However, these methods lack interpretability due to their complex structure, resulting in limited reliability. Addressing this issue, this study proposes a deep learning model that quantifies interpretability, with an emphasis on water level forecasting. This model focuses on generating quantitative interpretability measurements, which align with the common knowledge embedded in the input data. This is facilitated by the utilization of a transformer architecture that is purposefully designed with masking, incorporating a multi-layer network that captures spatiotemporal causation. We perform a comparative analysis on the Han River dataset obtained from Seoul, South Korea, from 2016 to 2021. The results illustrate that our approach offers enhanced interpretability consistent with common knowledge, outperforming competing methods and also enhances robustness against distribution shift.